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    "info": {
        "author": "Mehdi Ali",
        "author_email": "mehdi.ali@cs.uni-bonn.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "PyKEEN |build| |coverage| |docs| |zenodo|\n=========================================\n\nPyKEEN (Python KnowlEdge EmbeddiNgs) is a package for training and evaluating knowledge graph embeddings. Currently,\nit provides implementations of 10 knowledge graph emebddings models, and can be run in *training mode* in which users\nprovide their own set of hyper-parameter values, or in *hyper-parameter optimization mode* to find suitable\nhyper-parameter values from set of user defined values. PyKEEN can also be run without having experience in programing\nby using its interactive command line interface that can be started with the command *pykeen* from a terminal.\n\n\nNews\n----\nWe are currently working on PyKEEN 0.1.0 which will provide additional features such as several negative\nsampling approaches and further evaluation metrics. Furthermore, we are integrating additional KGE models.\n\nWe are developing a new `package <https://github.com/SmartDataAnalytics/POEM>`_ for training and evaluating multimodal\nKGE models which will be later integrated into PyKEEN.\n\nCitation\n--------\nIf you find PyKEEN useful in your work, please consider citing:\n\n.. [1] Ali, M., et al.: The KEEN Universe: An ecosystem for knowledge graph embeddings with a focus on reproducibility\n   and transferability. In: International Semantic Web Conference (2019).\n\nShare Your Experimental Artifacts\n---------------------------------\nYou can share you trained KGE models along the other experimental artifacts through the `KEEN Model Zoo <https://github.com/SmartDataAnalytics/KEEN-Model-Zoo>`_.\n\n\nInstallation |pypi_version| |python_versions| |pypi_license|\n------------------------------------------------------------\nTo install ``pykeen``, Python 3.6+ is required, and we recommend to install it on Linux or Mac OS systems.\nPlease run following command:\n\n.. code-block:: sh\n\n    pip install pykeen\n\nAlternatively, it can be installed from the source for development with:\n\n.. code-block:: sh\n\n    $ git clone https://github.com/SmartDataAnalytics/PyKEEN.git pykeen\n    $ cd pykeen\n    $ pip install -e .\n\nHowever, GPU acceleration is limited to Linux systems with the appropriate graphics cards\nas described in the PyTorch documentation.\n\nInstalling Extras with Pip\n--------------------------\nPyKEEN uses pip's extras functionality to allow some non-essential features to be skipped. They can be installed with\nthe following:\n\n1. ``pip install pykeen[ndex]`` enables support for loading networks from NDEx. They can be added to the training file\n   paths by prefixing files with ``ndex:``\n\nContributing\n------------\nContributions, whether filing an issue, making a pull request, or forking, are appreciated.\nSee `CONTRIBUTING.rst <https://github.com/SmartDataAnalytics/PyKEEN/blob/master/CONTRIBUTING.rst>`_ for more\ninformation on getting involved.\n\nTutorials\n---------\nCode examples can be found in the `notebooks directory\n<https://github.com/SmartDataAnalytics/PyKEEN/tree/master/notebooks>`_.\n\nFurther tutorials are available in our `documentation <https://pykeen.readthedocs.io/en/latest/>`_.\n\nCLI Usage - Set Up Your Experiment within 60 seconds\n----------------------------------------------------\nTo start the PyKEEN CLI, run the following command:\n\n.. code-block:: sh\n\n    pykeen\n\nthen the command line interface will assist you to configure your experiments.\n\nTo start PyKEEN with an existing configuration file, run:\n\n.. code-block:: sh\n\n    pykeen -c /path/to/config.json\n\nthen the command line interface won't be called, instead the pipeline will be started immediately.\n\nStarting the Prediction Pipeline\n********************************\nTo make prediction based on a trained model, run:\n\n.. code-block:: sh\n\n    pykeen-predict -m /path/to/model/directory -d /path/to/data/directory\n\nwhere the value for the argument **-m** is the directory containing the model, in more detail following files must be\ncontained in the directory:\n\n* configuration.json\n* entities_to_embeddings.json\n* relations_to_embeddings.json\n* trained_model.pkl\n\nThese files are automatically created after model is trained (and evaluated) and exported in your\nspecified output directory.\n\nThe value for the argument **-d** is the directory containing the data for which inference should be applied, and it\nneeds to contain following files:\n\n* entities.tsv\n* relations.tsv\n\nwhere *entities.tsv* contains all entities of interest, and relations.tsv all relations. Both files should contain\na single column containing all the entities/relations. Based on these files, PyKEEN will create all\ntriple permutations, and computes the predictions for them, and saves them in data directory\nin *predictions.tsv*.\nNote: the model- and the data-directory can be the same directory as long as all required files are provided.\n\nOptionally, a set of triples can be provided that should be exluded from the prediction, e.g. all the triples\ncontained in the training set:\n\n.. code-block:: sh\n\n   pykeen-predict -m /path/to/model/directory -d /path/to/data/directory -t /path/to/triples.tsv\n\nHence, it is easily possible to compute plausibility scores for all triples that are not contained in the training set.\n\nSummarize the Results of All Experiments\n****************************************\nTo summarize the results of all experiments, please provide the path to parent directory containing all the experiments\nas sub-directories, and the path to the output file:\n\n.. code-block:: sh\n\n    pykeen-summarize -d /path/to/experiments/directory -o /path/to/output/file.csv\n\n.. |build| image:: https://travis-ci.org/SmartDataAnalytics/PyKEEN.svg?branch=master\n    :target: https://travis-ci.org/SmartDataAnalytics/PyKEEN\n    :alt: Build Status\n\n.. |zenodo| image:: https://zenodo.org/badge/136345023.svg\n    :target: https://zenodo.org/badge/latestdoi/136345023\n    :alt: Zenodo DOI\n\n.. |docs| image:: http://readthedocs.org/projects/pykeen/badge/?version=latest\n    :target: https://pykeen.readthedocs.io/en/latest/\n    :alt: Documentation Status\n\n.. |python_versions| image:: https://img.shields.io/pypi/pyversions/pykeen.svg\n    :alt: Supported Python Versions: 3.6 and 3.7\n\n.. |pypi_version| image:: https://img.shields.io/pypi/v/pykeen.svg\n    :alt: Current version on PyPI\n\n.. |pypi_license| image:: https://img.shields.io/pypi/l/pykeen.svg\n    :alt: MIT License\n\n.. |coverage| image:: https://codecov.io/gh/SmartDataAnalytics/PyKEEN/branch/master/graphs/badge.svg\n    :target: https://codecov.io/gh/SmartDataAnalytics/PyKEEN\n    :alt: Coverage Status on CodeCov\n\n\n",
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        "home_page": "https://github.com/SmartDataAnalytics/PyKEEN",
        "keywords": "Knowledge Graph Embeddings,Machine Learning,Data Mining,Linked Data",
        "license": "MIT",
        "maintainer": "Mehdi Ali",
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